Extended kalman filter based state of charge estimation with a comprehensive test environment and modelling guideline

In recent years, electric vehicles (EVs) and lithium-ion batteries have gained prominence thanks to their potential to reduce emissions, lower carbon footprints, and offer cost-effective operation and maintenance. Moreover, regulatory emission standards drive the transition to EVs, calling for innovation in this domain. The thesis outlines three primary goals: first, modeling a robust extended Kalman filter (EKF) based state of charge (SOC) estimation algorithm tested across various scenarios; second, establishing a comprehensive testing framework, from unit testing to model in the loop (MIL) and software in the loop (SIL) testing; and finally, following to automotive industry modeling standards like ISO-26262 and MISRA C-2012 to ensure the model and code meet industry standards. This work begins by introducing battery management system (BMS) and SOC estimation, followed by an in-depth literature review covering different methods for the SOC estimation. The literature review examines trade-offs between simplicity, accuracy, computational complexity, sensitivity to noise, and computational cost of various SOC estimation methods. Additionally, battery cell characterization, EKF mathematical equations, EKF algorithm modeling, comprehensive test environment, and compliance with industry modeling standards are discussed in detail. The thesis seeks to contribute to the dynamic field of electric vehicle engineering and battery management.